Skip to main content
Sage Kokjohn

Sage Kokjohn

Phil and Jean Myers Professor
gscholar website
lab website

Professor Kokjohn uses detailed modeling and experiments to explain the mechanisms controlling high-efficiency energy conversion systems. His areas of interest include turbulent combustion model development and identification of pathways to achieve robust, high-efficiency energy conversion. He has published over 100 articles related to energy research in academic journals and conference proceedings and has been issued three US patents. Since joining the University of Wisconsin – Madison in 2013, Prof. Kokjohn has secured approximately $14M in external funding to support basic and applied energy research. He currently has ongoing projects funded by the Department of Energy (DOE), the Department of Defense (DOD), Caterpillar, and Ford.

Department

Mechanical Engineering

Contact

123, Engineering Research Building
1500 Engineering Dr
Madison, WI
p: (608) 263-1610

Professor Sage Kokjohn, Mechanical Engineering, University of Wisconsin-Madison

  • PhD 2012, University of Wisconsin-Madison
  • MS 2008, University of Wisconsin-Madison
  • BS 2006, Iowa State University

  • High efficiency energy conversion
  • Thermodynamics and fluid mechanics
  • Turbulent combustion modelling

  • 2023 ASME, ASME ICE Division Best Paper Award
  • 2019 College of Engineering, University of Wisconsin-Madison, Leaders in Engineering Excellence and Diversity (LEED) Scholars Program
  • 2018 SAE, SAE Max Bentele Award for Engine Technology Innovation
  • 2018 SAE, SAE Ralph R. Teetor Educational Award
  • 2014 ASME, Undergraduate Student Presentation Competition for ASME Internal Combustion Engine Division’s Fall Technical Conference
  • 2012 SAE, SAE Excellence in Oral Presentation Award
  • 2010 Nelson Institute Climate Leadership Challenge, Most Innovative Technical Solution

  • Sapra, H., Hessel, R., Miganakallu, N., Stafford, J., Amezcua, E., Rothamer, D., Kim, K., Kweon, C. M., & Kokjohn, S. (2024). Computational fluid dynamics and Machine learning-based Piston-Bowl optimization for Energy-Assisted compression ignition of low cetane number sustainable aviation fuel blends. Energy Conversion and Management, 300 https://doi.org/10.1016/j.enconman.2023.117929

  • M E 569 - Applied Combustion (Spring 2025)
  • M E 790 - Master's Research and Thesis (Spring 2025)
  • M E 890 - PhD Research and Thesis (Spring 2025)
  • M E 273 - Engineering Problem Solving with EES (Fall 2024)
  • M E 352 - Interdisciplinary Experiential Design Projects II (Fall 2024)
  • M E 469 - Internal Combustion Engines (Fall 2024)
  • M E 790 - Master's Research and Thesis (Fall 2024)
  • M E 890 - PhD Research and Thesis (Fall 2024)
  • M E 273 - Engineering Problem Solving with EES (Summer 2024)
  • M E 790 - Master's Research and Thesis (Summer 2024)
  • M E 890 - PhD Research and Thesis (Summer 2024)
  • M E 351 - Interdisciplinary Experiential Design Projects I (Spring 2024)
  • M E 699 - Advanced Independent Study (Spring 2024)
  • M E 790 - Master's Research and Thesis (Spring 2024)
  • M E 890 - PhD Research and Thesis (Spring 2024)
  • E P D 621 - Batteries for xElectrified Vehicles (Fall 2023)
  • M E 273 - Engineering Problem Solving with EES (Fall 2023)
  • M E 469 - Internal Combustion Engines (Fall 2023)
  • M E 790 - Master's Research and Thesis (Fall 2023)
  • M E 890 - PhD Research and Thesis (Fall 2023)
  • M E 273 - Engineering Problem Solving with EES (Summer 2023)
  • M E 790 - Master's Research and Thesis (Summer 2023)
  • M E 890 - PhD Research and Thesis (Summer 2023)
  • M E 990 - Dissertator Research and Thesis (Summer 2023)